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feat(Data): create DataSource, DataCollection, added hourly crypto data (#96)

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* feat(Data): create DataSource, DataCollection, added hourly crypto data

* fix(Data): hourly data format, loading & config
This commit is contained in:
Mark Aron Szulyovszky
2021-12-31 19:04:27 +01:00
committed by GitHub
parent f762ceed2a
commit 442915f847
59 changed files with 276175 additions and 43473 deletions
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feature_extractors/feature_extractor_presets.py
+1 -13
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@@ -1,5 +1,5 @@
from feature_extractors.feature_extractors import feature_lag, feature_mom, feature_ROC, feature_RSI, feature_STOD, feature_STOK, feature_vol, feature_day_of_month, feature_day_of_week, feature_month, feature_debug_future_lookahead
from utils.typing import FeatureExtractorConfig
from utils.types import FeatureExtractorConfig
from utils.helpers import flatten
from feature_extractors.fractional_differentiation import feature_fractional_differentiation
@@ -38,15 +38,3 @@ def preprocess_feature_extractors_config(data_dict: dict) -> dict:
preset_names = data_dict[key]
data_dict[key] = flatten([presets[preset_name] for preset_name in preset_names])
return data_dict
# Use this if ever we want to create an independent boolean for each featureextractor
# def preprocess_feature_extractors_config(data_dict: dict) -> dict:
# prefixes = ['own_features', 'other_features']
# features_dict = dict()
# for prefix in prefixes:
# features_to_include = [key.replace(prefix + "_", "") for key, value in data_dict.items() if key.startswith(prefix) and value == True]
# features_dict[prefix] = flatten([presets[feature_name] for feature_name in features_to_include])
# data_dict = {k: v for k, v in data_dict.items() if not (k.startswith(prefixes[0]) or k.startswith(prefixes[1]))}
# return (data_dict | features_dict)